from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""input_features""", """attention_mask"""]







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Tuple=80   ,   lowerCamelCase_						:						List[Any]=1_6000   ,   lowerCamelCase_						:						Union[str, Any]=0.0   ,   lowerCamelCase_						:						int=10   ,   lowerCamelCase_						:						Union[str, Any]=25   ,   lowerCamelCase_						:						List[Any]="hamming_window"   ,   lowerCamelCase_						:						Union[str, Any]=3_2_7_6_8.0   ,   lowerCamelCase_						:						Optional[int]=0.9_7   ,   lowerCamelCase_						:						List[str]=1.0   ,   lowerCamelCase_						:						List[Any]=True   ,   lowerCamelCase_						:						List[str]=True   ,   lowerCamelCase_						:						Optional[Any]=False   ,   **lowerCamelCase_						:						Optional[Any]   ,   ):





    """simple docstring"""


    super().__init__(feature_size=lowerCamelCase_   ,   sampling_rate=lowerCamelCase_   ,   padding_value=lowerCamelCase_   ,   **lowerCamelCase_				)

    UpperCamelCase			  =      feature_size
    UpperCamelCase			  =      sampling_rate
    UpperCamelCase			  =      padding_value
    UpperCamelCase			  =      hop_length
    UpperCamelCase			  =      win_length
    UpperCamelCase			  =      frame_signal_scale
    UpperCamelCase			  =      preemphasis_coeff
    UpperCamelCase			  =      mel_floor
    UpperCamelCase			  =      normalize_means
    UpperCamelCase			  =      normalize_vars
    UpperCamelCase			  =      win_function
    UpperCamelCase			  =      return_attention_mask

    UpperCamelCase			  =      win_length * sampling_rate // 1000
    UpperCamelCase			  =      hop_length * sampling_rate // 1000

    UpperCamelCase			  =      optimal_fft_length(self.sample_size				)
    UpperCamelCase			  =      (self.n_fft // 2) + 1







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						np.array				):





    """simple docstring"""


    if self.win_function == "hamming_window":
     UpperCamelCase			  =      window_function(window_length=self.sample_size   ,   name=self.win_function   ,   periodic=lowerCamelCase_				)
    else:
     UpperCamelCase			  =      window_function(window_length=self.sample_size   ,   name=self.win_function				)

    UpperCamelCase			  =      mel_filter_bank(
        num_frequency_bins=self.n_freqs   ,   num_mel_filters=self.feature_size   ,   min_frequency=0.0   ,   max_frequency=self.sampling_rate / 2.0   ,   sampling_rate=self.sampling_rate   ,   )

    UpperCamelCase			  =      spectrogram(
        one_waveform * self.frame_signal_scale   ,   window=lowerCamelCase_   ,   frame_length=self.sample_size   ,   hop_length=self.sample_stride   ,   fft_length=self.n_fft   ,   center=lowerCamelCase_   ,   preemphasis=self.preemphasis_coeff   ,   mel_filters=lowerCamelCase_   ,   mel_floor=self.mel_floor   ,   log_mel="""log"""   ,   )
    return msfc_features.T







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    if self.normalize_means:
     UpperCamelCase			  =      x[:input_length].mean(axis=0				)
     UpperCamelCase			  =      np.subtract(lowerCamelCase_   ,   lowerCamelCase_				)
    if self.normalize_vars:
     UpperCamelCase			  =      x[:input_length].std(axis=0				)
     UpperCamelCase			  =      np.divide(lowerCamelCase_   ,   lowerCamelCase_				)

    if input_length < x.shape[0]:
     UpperCamelCase			  =      padding_value

    # make sure array is in float32
    UpperCamelCase			  =      x.astype(np.floataa				)

    return x







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						List[np.ndarray]   ,   lowerCamelCase_						:						Optional[np.ndarray] = None				):





    """simple docstring"""


    UpperCamelCase			  =      attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
    return [self._normalize_one(lowerCamelCase_   ,   lowerCamelCase_   ,   self.padding_value				) for x, n in zip(lowerCamelCase_   ,   lowerCamelCase_				)]







   def __call__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,   lowerCamelCase_						:						Union[bool, str, PaddingStrategy] = False   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   **lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
      raise ValueError(
          f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
          f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
          f""" {self.sampling_rate} and not {sampling_rate}."""				)
    else:
     logger.warning(
         """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
         """Failing to do so can result in silent errors that might be hard to debug."""				)

    UpperCamelCase			  =      isinstance(lowerCamelCase_   ,   np.ndarray				) and len(raw_speech.shape				) > 1
    if is_batched_numpy and len(raw_speech.shape				) > 2:
     raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""				)
    UpperCamelCase			  =      is_batched_numpy or (
        isinstance(lowerCamelCase_   ,   (list, tuple)				) and (isinstance(raw_speech[0]   ,   (np.ndarray, tuple, list)				))
    )

    if is_batched:
     UpperCamelCase			  =      [np.asarray(lowerCamelCase_   ,   dtype=np.floataa				) for speech in raw_speech]
    elif not is_batched and not isinstance(lowerCamelCase_   ,   np.ndarray				):
     UpperCamelCase			  =      np.asarray(lowerCamelCase_   ,   dtype=np.floataa				)
    elif isinstance(lowerCamelCase_   ,   np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
     UpperCamelCase			  =      raw_speech.astype(np.floataa				)

    # always return batch
    if not is_batched:
     UpperCamelCase			  =      [raw_speech]

    # extract fbank features
    UpperCamelCase			  =      [self._extract_mfsc_features(lowerCamelCase_				) for one_waveform in raw_speech]

    # convert into correct format for padding
    UpperCamelCase			  =      BatchFeature({"""input_features""": features}				)

    UpperCamelCase			  =      self.pad(
        lowerCamelCase_   ,   padding=lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   truncation=lowerCamelCase_   ,   pad_to_multiple_of=lowerCamelCase_   ,   return_attention_mask=lowerCamelCase_   ,   **lowerCamelCase_   ,   )
    # make sure list is in array format
    UpperCamelCase			  =      padded_inputs.get("""input_features"""				)
    if isinstance(input_features[0]   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [np.asarray(lowerCamelCase_   ,   dtype=np.floataa				) for feature in input_features]

    UpperCamelCase			  =      padded_inputs.get("""attention_mask"""				)
    if attention_mask is not None:
     UpperCamelCase			  =      [np.asarray(lowerCamelCase_   ,   dtype=np.intaa				) for array in attention_mask]

    if self.normalize_means or self.normalize_vars:
     UpperCamelCase			  =      (
         np.array(lowerCamelCase_   ,   dtype=np.intaa				)
         if self._get_padding_strategies(lowerCamelCase_   ,   max_length=lowerCamelCase_				) is not PaddingStrategy.DO_NOT_PAD
         and padding
         else None
     )
     UpperCamelCase			  =      self.normalize(
         padded_inputs["""input_features"""]   ,   attention_mask=lowerCamelCase_				)

    if return_tensors is not None:
     UpperCamelCase			  =      padded_inputs.convert_to_tensors(lowerCamelCase_				)

    return padded_inputs

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
